Class Hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

CBase
CFST::Clonable_Impl< Derived, Base, DEEP_KIND, SHARING_KIND, STATELESS_KIND >	CRTP mixin implementing the Clonable protocol for a concrete class
CFST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DIST_L2 >
CFST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DIST_L1 >
CFST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DIST_LP >
CFST::Criterion_Wrapper< RETURNTYPE, SUBSET, REGRESSOR, DATAACCESSOR >
CFST::Data_Accessor_Splitting_MemARFF< DATATYPE, IDXTYPE, INTERVALLER >
CFST::Data_Accessor_Splitting_MemCSV< DATATYPE, IDXTYPE, INTERVALLER >
CFST::Data_Accessor_Splitting_MemTRN< DATATYPE, IDXTYPE, INTERVALLER >
CFST::Data_Accessor_Universe< DATATYPE, IDXTYPE, INTERVALLER >
CFST::Data_Splitter_5050< INTERVALLER, IDXTYPE >
CFST::Data_Splitter_CV< INTERVALLER, IDXTYPE >
CFST::Data_Splitter_Holdout< INTERVALLER, IDXTYPE >
CFST::Data_Splitter_Leave1Out< INTERVALLER, IDXTYPE >
CFST::Data_Splitter_RandomRandom< INTERVALLER, IDXTYPE, BINTYPE >
CFST::Data_Splitter_Resub< INTERVALLER, IDXTYPE >
CFST::Data_Splitter_TrainRandom_TestFixed< INTERVALLER, IDXTYPE, BINTYPE >
CFST::Distance_Lp< DATATYPE, REALTYPE, DIMTYPE, SUBSET, 3, 2 >
CFST::Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DIST_L2 >
CFST::Result_Weighter_Sampled< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::BatchEntry	One buffered, not-yet-flushed criterion evaluation: its raw value, the cardinality of the evaluated subset, and the list of selected feature indices — everything flush_batch() needs to z-standardize the batch and fold it into the accumulators
CFST::Data_Accessor< DATATYPE, IDXTYPE >::Block	One consecutive block of patterns as yielded by Block_Range/blocks()
CFST::Data_Accessor< DATATYPE, IDXTYPE >::Block_Range	RAII range over consecutive data blocks of one DataPart in the current split
CFST::Branch_And_Bound_Predictor< RETURNTYPE, DIMTYPE >	Defines Interface for Implementations of Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
CFST::Branch_And_Bound_Predictor_Averaging< RETURNTYPE, DIMTYPE >	Averaging Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
CFST::Candidate_Evaluator< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Abstract candidate-evaluation strategy (sequential-inline vs. thread-pooled)
CFST::Candidate_Evaluator_Sequential< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements sequential in-line evaluation of a criterion on a set of subset candidates
CFST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements concurrent evaluation of a criterion on a set of subset candidates
CFST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::CandidateResult	Temporary storage of subset candidate evaluation results
Cfst4_cli::RunResult::ClassCurve	One class's ROC and PR curves (see extract_classifier_curves())
CFST::Clonable	Abstract class, defines interface for classes that may need to get cloned
CFST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Abstract class, defines interface for classifier implementations (mainly to be used in wrappers)
CFST::Clonable_Impl< Classifier_Logistic_Regression, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Clonable_Impl< Classifier_Multinomial_NaiveBayes, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Clonable_Impl< Classifier_Normal_Bayes, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Clonable_Impl< Classifier_LIBSVM, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, CLONE_TO_STATELESS, CLONE_INHERIT, CLONE_WEAK >
CFST::Clonable_Impl< Classifier_Extern< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Extern< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Bridge classifier delegating training and prediction to a user-supplied external command
CFST::Clonable_Impl< Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_LDA< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Linear (Fisher/Gaussian shared-covariance) discriminant classifier
CFST::Clonable_Impl< Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, CLONE_TO_STATELESS, CLONE_INHERIT, CLONE_WEAK >
CFST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST4 classifier
CFST::Clonable_Impl< Classifier_Linear_SVM< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Linear_SVM< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Dependency-free linear SVM trained by dual coordinate descent (the method of Hsieh et al., ICML 2008, as popularized by LIBLINEAR — implemented from scratch, no external code)
CFST::Clonable_Impl< Classifier_Logistic_Regression< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Logistic_Regression< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements multinomial (softmax) logistic regression classifier
CFST::Clonable_Impl< Classifier_Multinomial_NaiveBayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Multinomial_NaiveBayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Naive-like Bayes classifier based on multinomial model
CFST::Clonable_Impl< Classifier_Normal_Bayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Normal_Bayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Bayes classifier based on normal (gaussian) model
CFST::Clonable_Impl< Classifier_RBF_SVM< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_RBF_SVM< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Dependency-free RBF kernel SVM trained by kernel dual coordinate descent (implemented from scratch, no LibSVM code; C-SVC with the RBF kernel only, deliberately NOT a LibSVM replacement: no nu-variants, no SVR, no one-class, no probability estimates, no shrinking)
CFST::Clonable_Impl< Classifier_Random_Forest< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_Random_Forest< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Dependency-free Random Forest classifier (CART trees, Gini impurity, bagging, per-node feature subsampling), deterministic by design
CFST::Clonable_Impl< Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >, Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >	Implements k-Nearest Neighbor classifier
CFST::Criterion< RETURNTYPE, SUBSET >	Abstract class, defines interface for feature selection criterion classes
CFST::Clonable_Impl< Criterion_Negative, Criterion< RETURNTYPE, SUBSET > >
CFST::Clonable_Impl< Criterion_Subset_Size, Criterion< RETURNTYPE, SUBSET > >
CFST::Clonable_Impl< Criterion_Sum_Of_Weights, Criterion< RETURNTYPE, SUBSET > >
CFST::Clonable_Impl< Criterion_Wrapper, Criterion< RETURNTYPE, SUBSET > >
CFST::Clonable_Impl< Criterion_Extern_Value< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion< RETURNTYPE, SUBSET > >
CFST::Criterion_Extern_Value< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	External-tool criterion: a user-supplied command computes the criterion value itself - unlike wrapper-extern, which exchanges labels and computes accuracy on the FST4 side
CFST::Clonable_Impl< Criterion_Negative< CRITERION, RETURNTYPE, SUBSET >, Criterion< RETURNTYPE, SUBSET > >
CFST::Criterion_Negative< CRITERION, RETURNTYPE, SUBSET >	Returns the negative of another Criterion's result
CFST::Clonable_Impl< Criterion_Subset_Size< RETURNTYPE, SUBSET >, Criterion< RETURNTYPE, SUBSET > >
CFST::Criterion_Subset_Size< RETURNTYPE, SUBSET >	Returns the (positive) subset size sub->get_d() - since FST4 searches maximize, this rates LARGER subsets higher. Wrap in Criterion_Negative to favor smaller subsets instead
CFST::Clonable_Impl< Criterion_Sum_Of_Weights< RETURNTYPE, DIMTYPE, SUBSET >, Criterion< RETURNTYPE, SUBSET > >
CFST::Criterion_Sum_Of_Weights< RETURNTYPE, DIMTYPE, SUBSET >	Returns sum of pre-specified feature weights for features in the evaluated subset
CFST::Clonable_Impl< Criterion_Wrapper< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >, Criterion< RETURNTYPE, SUBSET > >
CFST::Criterion_Wrapper< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >	Wraps Classifier objects to serve as feature selection criterion
CFST::Clonable_Impl< Criterion_Wrapper_Bias_Estimate< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >, Criterion_Wrapper< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR > >
CFST::Criterion_Wrapper_Bias_Estimate< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >	Wrapper Bias Evaluator - returns bias estimate (difference of classifier accuracy on training and validation data parts)
CFST::Criterion_Filter< RETURNTYPE, SUBSET >	Abstract class, defines interface for classes implementing "independent" feature selection criteria
CFST::Criterion_Multinomial< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Abstract class, defines interface for feature selection criterion implementations based on multinomial model
CFST::Clonable_Impl< Criterion_Multinomial_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion_Multinomial< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Criterion_Multinomial_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion
CFST::Clonable_Impl< Criterion_Multinomial_MI< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion_Multinomial< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Criterion_Multinomial_MI< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements individual Mutual Information based on multinomial model to serve as feature selection criterion in Best Individual Feature setting (feature ranking)
CFST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Abstract class, defines interface for feature selection criterion implementations based on normal model
CFST::Clonable_Impl< Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion
CFST::Clonable_Impl< Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion
CFST::Clonable_Impl< Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion
CFST::Data_Accessor< DATATYPE, IDXTYPE >	Abstract class, defines interface for data access implementations
CFST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Partly-abstract class, defines support for data splitting
CFST::Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALLER >
CFST::Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Implements data access to data cached entirely in memory, concrete file type support is delegated to derived classes
CFST::Clonable_Impl< Data_Accessor_Splitting_MemARFF< DATATYPE, IDXTYPE, INTERVALCONTAINER >, Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Data_Accessor_Splitting_MemARFF< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Implements data access to data cached entirely in memory, read once from a ARFF file
CFST::Clonable_Impl< Data_Accessor_Splitting_MemCSV< DATATYPE, IDXTYPE, INTERVALCONTAINER >, Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Data_Accessor_Splitting_MemCSV< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Implements data access to data cached entirely in memory, read once from a CSV (delimited text) file; see Data_File_CSV for the accepted dialects and options
CFST::Clonable_Impl< Data_Accessor_Splitting_MemTRN< DATATYPE, IDXTYPE, INTERVALCONTAINER >, Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Data_Accessor_Splitting_MemTRN< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Implements data access to data cached entirely in memory, read once from a TRN file
CFST::Clonable_Impl< Data_Accessor_Universe< DATATYPE, IDXTYPE, INTERVALCONTAINER >, Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Data_Accessor_Universe< DATATYPE, IDXTYPE, INTERVALCONTAINER >	Data-free accessor over an abstract feature universe of N features (the fst4 CLI's --features N entry; the companion of criterion_extern_value.hpp in its data-free mode)
CFST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >	Abstract class, defines interface for data splitting implementations (for use in data accessors)
CFST::Clonable_Impl< Data_Splitter_5050, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_CV, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_Holdout, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_Leave1Out, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_RandomRandom, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_Resub, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Data_Splitter_5050< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_5050< INTERVALCONTAINER, IDXTYPE >	Implements train/test data splitting: first 50% of original data for training, second 50% for testing, each (data)class split separately
CFST::Clonable_Impl< Data_Splitter_CV< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_CV< INTERVALCONTAINER, IDXTYPE >	Implements train/test data splitting: by means of k-fold cross-validation
CFST::Clonable_Impl< Data_Splitter_Holdout< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_Holdout< INTERVALCONTAINER, IDXTYPE >	Implements train/test data splitting: use initial x% of original data for training, the rest for testing, each (data)class split separately
CFST::Clonable_Impl< Data_Splitter_Leave1Out< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_Leave1Out< INTERVALCONTAINER, IDXTYPE >	Implements train/test data splitting: by means of leave-one-out
CFST::Clonable_Impl< Data_Splitter_RandomRandom< INTERVALCONTAINER, IDXTYPE, BINTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_RandomRandom< INTERVALCONTAINER, IDXTYPE, BINTYPE >	Implements train/test data splitting: use randomly chosen x% of data samples for training and another y% of data for testing, without overlaps, separately in each class
CFST::Clonable_Impl< Data_Splitter_TrainRandom_TestFixed< INTERVALCONTAINER, IDXTYPE, BINTYPE >, Data_Splitter_RandomRandom< INTERVALCONTAINER, IDXTYPE, BINTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_TrainRandom_TestFixed< INTERVALCONTAINER, IDXTYPE, BINTYPE >	Implements train/test data splitting: equal to Data_Splitter_RandomRandom except that the same test data is returned in each loop
CFST::Clonable_Impl< Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >	Implements train/test data splitting: use all data both for training and then once more for testing
CFST::Distance< DATATYPE, DIMTYPE, SUBSET >	Abstract class, defines interface for distance evaluators to be used in Classifier_kNN
CFST::Clonable_Impl< Distance_Euclid< DATATYPE, DIMTYPE, SUBSET >, Distance< DATATYPE, DIMTYPE, SUBSET > >
CFST::Distance_Euclid< DATATYPE, DIMTYPE, SUBSET >	Implements Euclidean (L_2) distance
CFST::Clonable_Impl< Distance_L1< DATATYPE, DIMTYPE, SUBSET >, Distance< DATATYPE, DIMTYPE, SUBSET > >
CFST::Distance_L1< DATATYPE, DIMTYPE, SUBSET >	Implements L_1 (city-block / Manhattan) distance
CFST::Clonable_Impl< Distance_Lp< DATATYPE, REALTYPE, DIMTYPE, SUBSET, 1, 1 >, Distance< DATATYPE, DIMTYPE, SUBSET > >
CFST::Distance_Lp< DATATYPE, REALTYPE, DIMTYPE, SUBSET, numerator, denominator >	Implements L_{p} distance, p equals numerator/denominator (template parameters)
CFST::Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Abstract class, defines interface for Regressor implementations (mainly to be used in wrappers)
CFST::Clonable_Impl< Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >, Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Regressor_Ridge< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Closed-form ridge regression (L2-regularized least squares) for wrapper-based feature selection
CFST::Clonable_Impl< Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >, Regressor< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > >
CFST::Regressor_Weighted_Neighbours< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >	Implements experimental regressor that produces weighted average of the predicted variable over neighbours, weighted by distance
CFST::Result_Tracker< RETURNTYPE, SUBSET >	Abstract class, defines interface for classes that enable collecting multiple results
CFST::Clonable_Impl< Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >, Result_Tracker< RETURNTYPE, SUBSET >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >	Collects multiple results, avoiding duplicates
CFST::Clonable_Impl< Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >, Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >	Collects evaluated subsets to eventually provide Dependency-Aware Feature Ranking coefficients
CFST::Clonable_Impl< Result_Tracker_Regularizer< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, CRITERION >, Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Result_Tracker_Regularizer< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, CRITERION >	Collects multiple results. Enables eventual selection of alternative solution based on secondary criterion
CFST::Clonable_Impl< Result_Tracker_Stability_Evaluator< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >, Result_Tracker< RETURNTYPE, SUBSET >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Result_Tracker_Stability_Evaluator< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >	Collects multiple results to evaluate various stability measures on the collection
Cfst4_cli::CompareResult	Everything produced by a --compare-config second scenario (the demo55 protocol)
Cfst4_cli::Config	All fst4 CLI settings in one place: one field per command-line flag (plus a few derived/internal fields such as data_resolved), populated by CLI11 in add_options() and consumed by the factory functions and run_pipeline() below. Unless noted, string fields default to "" (flag omitted / feature off); concrete defaults live in the Config() constructor below
CCONTAINER
CFST::iterator< CONTAINER, IDXTYPE >
CFST::Data_Intervaller< CONTAINER, IDXTYPE >	Container to hold list of Data_Interval; implements nested interval reduction
CCRITERION
CFST::Clonable_Impl< Criterion_Counting, CRITERION >
Cfst4_cli::Criterion_Counting
Cfst4_cli::RunResult::CurveEntry	One curve_over_d point: the best subset of a given size plus its holdout re-validation (the overfitting curve)
Cfst4_cli::RunResult::CurvePoint	One ROC/PR point at a given decision threshold (see extract_curves())
Cfst4_cli::RunResult::DafEntry	One --daf-rank ranked feature
CDASM
CFST::Clonable_Impl< Data_Accessor_Splitting_MemARFF, DASM, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Clonable_Impl< Data_Accessor_Splitting_MemCSV, DASM, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Clonable_Impl< Data_Accessor_Splitting_MemTRN, DASM, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Clonable_Impl< Data_Accessor_Universe, DASM, CLONE_INHERIT, CLONE_WEAK_SHARING, CLONE_INHERIT >
CFST::Data_File_ARFF	ARFF data format filter
CFST::Data_File_ARFF_Feature	ARFF data format filter - representation of an attribute (one @ATTRIBUTE column)
CFST::Data_File_ARFF_Record	ARFF data format filter - representation of a record (one @DATA row)
CFST::Data_File_CSV	CSV (delimited text) data format filter — the simplest spreadsheet-to-FST4 path
CFST::Data_File_CSV_Options	CSV loader options
CFST::Data_File_TRN< DATATYPE, IDXTYPE >	TRN data format filter
CFST::Data_Interval< IDXTYPE >	Support structure to hold data interval (referring into data held by data accessor)
CFST::Data_Scaler< DATATYPE >	Abstract class, defines interface for data scaling implementations (for use in data accessors)
CFST::Data_Scaler_to01< DATATYPE >	Implements data normalization (of all feature values) to interval [0,1]
CFST::Data_Scaler_void< DATATYPE >	Void data scaler, to bypass data normalization
CFST::Data_Scaler_white< DATATYPE, IDXTYPE >	Implements data whitening (as result, each feature mean value is 0 with normalized stddev)
CFST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER >::DataSplit	Data splitting support structure; holds one set of intervals (train, test) per each splitting depth and (data)class
CFST::Extern_Bridge	Persistent-child-process line-protocol plumbing shared by the external-tool bridges: Classifier_Extern (TRAIN/TEST label protocol) and Criterion_Extern_Value (VALUE protocol)
CFST::Subset< BINTYPE, DIMTYPE >::Feature_Range	Read-only forward range over feature indices
CFST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::FeatureStat	Structure to gather feature occurrence statistics over probe subset evaluations
CFST::Result_Weighter_Sampled< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::FeatureStat	Per-feature accumulators, one instance per feature index, updated by flush_batch(). m_is/m_isnot are adaptive-rate running means of the batch z-score, one side for evaluations in which the feature was selected ('is') and one for evaluations in which it was not ('isnot'); score() in STATS_ZCONTRAST mode returns their difference. raw_sum_is/raw_cnt_is accumulate the STATS_RAWMEAN statistic instead. n_is/n_isnot count how many evaluations landed on each side — also the exploration counts exposed via count_is()/count_isnot()
CFST::Indexed_Matrix< DATATYPE, DIMTYPE, SUBSET >	Matrix representation and operations, allows operation in selected subspace
CFST::Indexed_Vector< DATATYPE, DIMTYPE, SUBSET >	Vector representation and operations, allows operation in selected subspace. Also serves as the helper structure for passing matrix-operation intermediate data between higher-level matrix objects and functions
CFST::Data_Accessor< DATATYPE, IDXTYPE >::Block_Range::iterator	Forward iterator over Block_Range; drives the wrapped getFirstBlock()/getNextBlock() cursor at the range's owned loopdepth
CFST::Subset< BINTYPE, DIMTYPE >::Feature_Range::iterator	Forward iterator over raw feature indices matching a Kind, skipping non-matching ones
CFST::Metric_Spec< RETURNTYPE >	Configuration of the wrapper metric; immutable during search, copied by criterion clones (thread-safe, RNG-free)
CFST::Model< SUBSET, DATAACCESSOR >	Abstract class, defines interface for data model implementations
CFST::Model_Multinomial< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Multinomial ("bag of words") model: per-class term-count sums and the Laplace-smoothed term probabilities P(term|class) derived from them
CFST::Model_Normal< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >	Implements normal (gaussian) model
CFST::msgbuf	Message accumulator for Search::report() - collects one progress message exactly as an std::ostringstream would, for delivery either to an attached Search_Observer or to the search's output stream
CFST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >::Neighbour	Holds information on distance and data-class membership of neighbors processed in Classifier_kNN
CFST::Classifier_Random_Forest< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::Node
CFST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::Node	Structure representing search tree node
CFST::Search_Ranking< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::OneFeature	Structure to hold [feature,criterion value] pair while ranking features
CFST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
CFST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
CFST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
Cfst4_cli::RunResult::PairDafEntry	One --daf-pairs pairwise (2-feature) DAF statistic
CFST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::PairStat	Structure to hold pairwise feature statistics (2x2 presence-cell contrasts over probe subset evaluations)
CFST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::ParameterSet	Nested class to hold parameter candidates in the course of optimize_parameters() run
Cparent
CFST::Clonable_Impl< Data_Splitter_TrainRandom_TestFixed, parent, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Classifier_Extern, parent >
CFST::Clonable_Impl< Classifier_kNN, parent >
CFST::Clonable_Impl< Classifier_LDA, parent >
CFST::Clonable_Impl< Classifier_Linear_SVM, parent >
CFST::Clonable_Impl< Classifier_RBF_SVM, parent >
CFST::Clonable_Impl< Classifier_Random_Forest, parent >
CFST::Clonable_Impl< Criterion_Extern_Value, parent >
CFST::Clonable_Impl< Criterion_Multinomial_Bhattacharyya, parent >
CFST::Clonable_Impl< Criterion_Multinomial_MI, parent >
CFST::Clonable_Impl< Criterion_Normal_Bhattacharyya, parent >
CFST::Clonable_Impl< Criterion_Normal_Divergence, parent >
CFST::Clonable_Impl< Criterion_Normal_GMahalanobis, parent >
CFST::Clonable_Impl< Criterion_Wrapper_Bias_Estimate, parent >
CFST::Clonable_Impl< Distance_Euclid, parent >
CFST::Clonable_Impl< Distance_L1, parent >
CFST::Clonable_Impl< Distance_Lp, parent >
CFST::Clonable_Impl< Regressor_Ridge, parent >
CFST::Clonable_Impl< Regressor_Weighted_Neighbours, parent >
CFST::Clonable_Impl< Result_Tracker_Dupless, parent, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Result_Tracker_Feature_Stats, parent, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Result_Tracker_Regularizer, parent, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
CFST::Clonable_Impl< Result_Tracker_Stability_Evaluator, parent, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
Cfst4_cli::Pipeline	Everything the run phase needs, assembled by the factories below
Cfst4_cli::RunResult::RegEntry	One --regularize-margins sweep point: the best subset within margin of the maximum by the secondary criterion
CFST::Result_Weighter_Sampled< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >	Online per-feature statistics for budgeted (sampled) sequential steps — discounted batch-standardized DAF contrast
CFST::Result_Tracker< RETURNTYPE, SUBSET >::ResultRec	Structure to hold [subset,criterion value] temporary solutions in the course of search
Cfst4_cli::RunResult
Cstd::runtime_error
CFST::fst_error	FST4 specific error passing class
CFST::Search< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Abstract class, defines interface for search method implementations
CFST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements Branch and Bound template method as basis for more advanced B&B implementations
CFST::Search_Branch_And_Bound_Basic< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements Branch and Bound Basic method, i.e., with randomized node ordering
CFST::Search_Branch_And_Bound_Fast< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR >	Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism
CFST::Search_Branch_And_Bound_Improved< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering
CFST::Search_Branch_And_Bound_Improved_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	DEPRECATED shim: threaded Improved Branch and Bound (IBB) method
CFST::Search_Branch_And_Bound_Partial_Prediction< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR >	Implements Branch and Bound with Partial Prediction (BBPP) method, i.e., with predicted node ordering
CFST::Search_Exhaustive< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion
CFST::Search_Exhaustive_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	DEPRECATED shim: threaded version of exhaustive (optimal) search
CFST::Search_Monte_Carlo< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Repeatedly samples random subsets to eventually yield the one with highest criterion value
CFST::Search_Monte_Carlo_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	DEPRECATED shim: threaded version of randomized search that repeatedly samples random subsets
CFST::Search_Ranking< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Abstract base of the feature-ranking methods: scores features one by one, then turns the ranking into a feature subset
CFST::Search_Ranking_BIF< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements Best Individual Features, i.e., individual feature ranking
CFST::Search_Ranking_BIF_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	DEPRECATED shim: threaded Best Individual Features, i.e., individual feature ranking
CFST::Search_Sequential< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Abstract class, defines interface for sequential search method implementations, provides common implementation of the "sequential step" operation
CFST::Search_DOS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Implements Dynamic_Oscillating_Search
CFST::Search_OS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Implements Oscillating_Search
CFST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Implements Sequential_Forward_Floating_Search and Sequential_Backward_Floating_Search
CFST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Implements Sequential_Forward_Retreating_Search and Sequential_Backward_Retreating_Search
CFST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >	Implements Sequential_Forward_Selection and Sequential_Backward_Selection
CFST::Search_Observer< RETURNTYPE, SUBSET >	Observer interface for search progress reporting
Cfst4_cli::Observer_Curve
Cfst4_cli::Observer_JSON	--progress json: emits one JSON line per improved solution to the progress stream; free-text progress is dropped
CFST::Sequential_Step< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Abstract class, defines interface for implementations of one selection step in sequential search type of methods
CFST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, CRITERION >
CFST::Sequential_Step_Sampled< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, WEIGHTER_SAMPLED >
CFST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements voting ensemble selection step in sequential search type of methods to possibly improve robustness and stability of feature selection result
CFST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION >	Implements hybrid selection step in sequential search type of methods
CFST::Sequential_Step_Sampled< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, WEIGHTER >	Implements the budgeted (sampled) sequential selection step: forward steps evaluate only a sampled batch of candidates, backward steps sweep in full unless a backward budget is set
CFST::Sequential_Step_Straight< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	Implements standard sequential selection step in sequential search type of methods
CFST::Sequential_Step_Straight_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >	DEPRECATED shim: threaded version of sequential selection step in sequential search type of methods
CFST::Data_File_ARFF::SkippedColumn	One @ATTRIBUTE column that was recognized but could not be loaded (STRING/DATE/unknown type), for the load-summary report
Cfst4_cli::RunResult::StabilityEntry	One named stability/similarity measure computed by the stabileval tracker (see the FST4_STAB macro)
Cfst4_cli::Stdin_Data_File
CFST::StopWatch	Simple stopwatch to measure run time of search algorithms
Cstd::streambuf
Cfst4_cli::NullBuf	Swallows all output (used with --verbosity silent)
CFST::Subset< BINTYPE, DIMTYPE >	Stores the info on currently selected features, enables generating permutations, etc
CFST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::SubsetCandidate	Nested class to hold feature/subset candidate info in the course of the ensemble voting process
CFST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION >::SubsetCandidate	Nested class to hold [Subset,criterion value] pair in the course of hybrid feature candidate evaluation in Sequential_Step_Hybrid
CFST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::SubSizeStat	Structure to gather probe subset cardinality statistics
Cfst4_cli::Svg_Panel
Cfst4_cli::RunResult::SweepEntry	One --regr-multipliers sweep point (demo11reg)
CFST::syncout	Serializes textual reporting from concurrent threads
CFST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >::TClassSplitter	Data splitting support structure; holds one set of intervals (train, test) per each (data)class
CFST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::ThreadLocal	Thread-local storage of current subset candidate, criterion clone, and tracker clone
CFST::ThreadPool	Implements thread scheduler that assigns jobs up to maximum number of threads allowed
CFST::ThreadPool::ThreadsArray	Structure to keep status of threads in the pool
Cfst4_cli::RunResult::TopEntry	One entry of the dupless tracker's --top-k best distinct subsets
Cfst4_cli::RunResult::TrialEntry	One --repeat-over-splits trial's search outcome
Cfst4_cli::RunResult::ValidationEntry	One classifier's/regressor's holdout validation outcome (--validate)
Cstd::vector
CFST::Data_Intervaller< std::vector< FST::Data_Interval< IDXTYPE > >, IDXTYPE >